Major Depressive Disorder (MDD) is unfortunately common with substantial burden of disease, economically and personally. Given the prevalence of depression and its debilitating course, [developing biomarkers that that have predictive value for the development, maintenance, and treatment of MDD and related disorders is of high scientific significance. Biomarkers that link deficits in neural systems to specific psychological processes that are dysfunctional in MDD] are especially valuable because they can reveal risk-to-symptom pathways that may be future targets for treatments and preventions. Although neuroimaging in MDD has generated impressive returns, imaging procedures such as functional magnetic resonance imaging (fMRI) are not well- suited for studying prospective of risk for MDD, given the relatively high cost of fMRI and the large samples required for prospective studies. A cost-effective and promising strategy would be to link less costly and more widely-available electroencephalographic (EEG) indices of brain activity to specific neural systems involved in MDD, [and subsequently to use these EEG biomarkers in assessing risk in research and clinical settings. Future prospective research using cost-effective EEG in large samples would have a clear link to established neural systems identified with fMRI approaches. Moreover, such easily-assessed biomarkers can promote premorbid risk assessment, facilitate early diagnosis, and lead to individually-tailored treatment and] prevention approaches for high-risk populations. With these goals in mind, [and motivated by a cognitive-neural emotion- regulation framework of depressive vulnerability,] we propose to collect simultaneous resting-state (RS) fMRI and 64-channel EEG data [from never-depressed and previously-depressed young adults], to identify associations between surface-recorded EEG and regional connectivity assessed via RSfMRI. We will apply cutting-edge approaches to the examination of RSfMRI networks and EEG data, including independent components analysis and multivariate vector approaches. We will examine EEG features motivated by extant EEG MDD literature, such as frontal EEG asymmetry, and also conduct broader exploratory analyses, to identify which EEG features index aspects of resting state network connectivity that have previously been identified as dysregulated in MDD. We can then assess whether these EEG features differentiate individuals with a lifetime history of MDD from those without - which would be expected of a risk indicator for MDD - using [the present sample and also] our extant sample of 306 individuals (143 with a history of MDD), all of whom have provided resting EEG data. In addition to the RSfMRI, high resolution T1 structural images as well as diffusion tensor images (DTI) will be collected to provide structural correlates of EEG and RSfMRI connectivity that can be examined in a highly exploratory manner. In this application we provide pilot data showing the feasibility o this approach, but consistent with the R21 mechanism, we consider our exploratory approach to be a strength of this proposal.